similar to: Cox model: random effect on a variable with 3 levels

After an extremely long gestation, a new version of the coxme package is now available on CRAN. This function fits very general mixed-effects Cox models using an lmer-like syntax. For instance here is a model that fits a random treatment effect, using data from a multi-study collaboration. fit <- coxme(Surv(pg.time, pg.stat) ~ stage + trt + (trt|site) + stata(site), data=

After an extremely long gestation, a new version of the coxme package is now available on CRAN. This function fits very general mixed-effects Cox models using an lmer-like syntax. For instance here is a model that fits a random treatment effect, using data from a multi-study collaboration. fit <- coxme(Surv(pg.time, pg.stat) ~ stage + trt + (trt|site) + stata(site), data=

Hi, I am new to R and AIC scores but what I get from coxme seems wrong. The AIC score increases as p-values decrease. Since lower AIC scores mean better models and lower p-values mean stronger effects or differences then shouldn't they change in the same direction? I found this happens with the data set rats as well as my own data. Below is the output for two models constructed with the rats

The coxme package has a ranef() method, as does lme4. I'm having trouble getting them to play together, as shown below. (The particular model in the example isn't defensible, but uses a standard data set.) The problem is that most of the time only one of lme4 or coxme will be loaded, so each needs to define the basic ranef function as well as a method for it. But when loaded together

User of the coxme library (mixed effects Cox models) are instructed to use ranef(), fixed(), VarCorr(), etc to retrieve bits out of a fitted model; it purposely uses the same methods as nlme and/or lmer. The current behavior is to "depend" on nlme. If I defined the methods myself in coxme, then someone who had both nlme and coxme loaded will suffer from "last loaded wins",

> > I'm testing out a new version of coxme and R CMD check fails with "could not find function > > ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the > > line below > > > importFrom(nlme, ranef, random.effects, fixef, fixed.effects, VarCorr) > > > and nlme is declared in the DESCRIPTION

Version 2.2 of coxme has been posted to CRAN, Windows versions and mirrors should appear in due course. This is a major update with three features of note: 1. A non-upwardly compatable change: Extractor functions: beta= fixed effects, b=random effects nlme lme4 coxme <2.2 coxme 2.2 lmekin 2.2 ------------------------------------------------------ beta

On 16/02/2015 8:20 AM, Therneau, Terry M., Ph.D. wrote: > >> > I'm testing out a new version of coxme and R CMD check fails with "could not find function >> > ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the >> > line below >> >> > importFrom(nlme, ranef, random.effects, fixef,

>>>>> Therneau, Terry M , Ph D <therneau at mayo.edu> >>>>> on Sun, 15 Feb 2015 17:31:00 -0600 writes: > I'm testing out a new version of coxme and R CMD check fails with "could not find function > ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the > line below >

Hello, i'm a french student of medical oncology and i'm working on breast cancer. I have a variable with the histologic type of tumor wich is between 1 and 5. I use as.factor function to make some variable with level between 1 and 5. When i put it in the cox model i have only the level between 2 and 5. The level 1 doesn't appear. I think i have to change the number of level but i

Dear R-users, I am a novice R-user with some experience in using the RMS package for taking nomograms after various survival models. This time, I am trying to plot a nomogram after a Random Effects Cox, implemented by the "coxme" package. My questions are: 1. Is it possible to take a nomogram directly after the coxme survival function? 2. If not is there a way to take the linear

The change has also been made to coxme. I'm in the midst of another fix (should be done by tomorrow) and will get posted to Rforge as soon as that's done. Terry On Thu, Mar 18, 2010 at 6:26 AM, Dieter Menne <dieter.menne at menne-biomed.de> wrote: > I am trying to use nlme and coxme in one Sweave document. I have read the > thread in r-devel on the subject, > >

Dear all, I would like to know if it is possible to fit in R a Cox ph model with time-dependent covariates and to account for hierarchical effects at the same time. Additionally, I'd like also to know if it would be possible to perform any feature selection on this model fit. I have a data set that is composed by multiple marker measurements (and hundreds of covariates) at different time

I'm testing out a new version of coxme and R CMD check fails with "could not find function ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the line below importFrom(nlme, ranef, random.effects, fixef, fixed.effects, VarCorr) and nlme is declared in the DESCRIPTION file as an import. I feel that I must be staring at some obvious (but

Dear R users, Here is the coxme output I obtain on my survival dataset having 3 strains and 2 infection status (i: infected, ni: non infected) coxme(Surv(lay) ~ infection*strain, data=datalay, random= ~1 |block) Cox mixed-effects model fit by maximum likelihood Data: datalay n= 1194 Iterations= 3 77 NULL Integrated Penalized Log-likelihood -7270.028 -7223.859 -7218.175

Thank you for Prof.Ripley's suggestion. I fixed the program by adding a lower scope, and the program ran, but I still got warning messages, and don't know what is going on, would this affect my results? ... Step: AIC= 12337.74 Surv(tlfup, cen) ~ MI[[j]]$trt + MI[[j]]$agem40 + MI[[j]]$agem40sq + mhtypeed1 + mhtypeed2 Df AIC <none> 12338 -

Here is an example of how to do it. > library(survival) > vfit <- coxph(Surv(time, status) ~ celltype + trt, data=veteran) > userinput <- data.frame(celltype="smallcell", trt = 1) > usercurve <- survfit(vfit, newdata=userinput) #the entire predicted survival curve > user2 <- summary(usercurve, time= 2*365.25) # 2 year time point > user2$surv [1]

Dear all, This probably stems from my lack of understanding of the model, but I do not understand the variance of the random effect reported in coxme. Consider the following toy example: #------------------------------- BEGINNING OF CODE ------------------------------------------------ library(survival) library(coxme) #--- Generate toy data: d <- data.frame(id = c(1:100), #

Hello all, I am a medical student and as a capstone for my summer research project I am going to create a simple online web "calculator" for users to input their relevant data, and a probability of relapse within 5 years will be computed and returned based on the Cox PH model I have developed. The issue I'm having is finding a definitive method/function to feed the user's

Hi I'm encountering an error/warning when doing multiple comparisons with the package multcomp on a coxme model. My data: I'm looking at the removal of brood from the nest according to three treatments I applied on the brood. The brood and the workers caring about the brood in the nest, belonged to different colonies. Factor: treatment (3 levels: tx,uv,meta) Random effect 1: origin of